Many applications in wireless communications utilize the location of a particular wireless device. Such location information may be used within an application itself, e.g. for navigating a user between locations. Additionally, location information may be utilized by the wireless network and/or the user device to better direct communications, to understand hardware/bandwidth allocations and the like.
In indoor networks, such as a local Wi-Fi network, the utilization of global positioning system (GPS) signals for location acquisition is not feasible. This is primarily because GPS signals are too attenuated to be received by devices in and indoor environment and therefore cannot be relied on to provide location information within a structure.
Because GPS is not an adequate indoor solution for location determinations of devices in a wireless network, other indoor localization methods have been implemented. Some of these methods utilize various radio frequency (RF) techniques such as triangulation, scene analysis and proximity-based methods.
In a triangulation method, a device transmits/receives a signal to/from three or more different known locations, and the location/position of the device is determined based on the angle of arrival, time of arrival, and signal strength directly to/from these known locations. These methods, however, suffer from multiple deficiencies. For example, in an indoor environment, walls, furniture, people and other moving obstacles are affecting radio waves randomly and therefore signals may arrive in unpredictable indirect path which affecting the angle/time of arrival, and appear weaker for reasons other than distance from a particular location. Therefore, multipath effects tend to interfere with triangulation techniques and degrading the accuracy of position determination.
Scene analysis methods utilize a rough estimation of the position of a device based on the ubiquitous radio frequency signals such as Wi-Fi signal, which becomes commonly found everywhere in the city. Prior to determining a position of a device, a network administrator must collect a database record of signal strengths from multiple transmission points at each position, known as radio map, within the network range. This is sometimes referred to as “fingerprinting” the network. When a wireless device is deployed in the network, the signal strengths observed from various points in the network can then be compared or referenced to the database record to see which location experienced similar signal strengths, thereby providing a position determination. The main drawback of scene analysis methods however, is that there is a relatively low degree of accuracy. For example, a position determination made using a scene analysis method may only be accurate up to around 5 to 10 meters depending on the no. of access points and how they were deployed. One benefit is that the wireless device is unlikely to be lost track.
Proximity-based methods utilize additional hardware resources, such as RFID beacons deployed throughout the wireless network and RFID tags on the various wireless devices. When a wireless device is near a beacon, which usually radiate in all directions, communication between the beacon and tag may provide information for acquiring positioning data. These methods provide similar or slightly better location resolution than scene analysis methods especially at some targeted locations with beacons. However, this still is a relatively low degree of accuracy because fuzzy location identification may occur as the tag may receive signal from more than one beacons, or the wireless device maybe lost track if no signals is received from any beacons. Further, proximity-based methods are disadvantageous because of the requirement to utilize additional hardware in order to accomplish positioning location tasks. This hardware adds additional expense and complications within the wireless network.
Some methods have attempted to combine scene analysis and proximity-based methods which utilize fingerprinting and beacon-based signals. These methods have produced a better degree of service (e.g. detection of position may up to around 3-5 meters of resolution at some specific locations and maintain better tracking of the device). However, such methods still require additional hardware resources and undergo fuzzy location identification as described above.